Participants (n = 27; 14 males, 13 females; mean age (SD) = 24.65 (4.21) years) incidentally encoded 120 vignettes, 15 from each of the different foundations postulated by Moral Foundations Theory (Haidt and Joseph, 2007; Graham et al., 2012) and 15 depicting transgressions of amoral social norms. This took place inside the scanner, where they had 6 seconds to make a moral judgment about each (1-4: not morally wrong-extremely morally wrong).
The memory test took place outside of the scanner. Participants were cued with each vignette presented during study + 8 lures per foundation (including social norms, only 7 lures were presented for Purity). Each vignette had up to 4 words removed, and participants were asked to select which word or words completed the vignette as well as their confidence in their response (not confident-extremely confident; 1-4).
After the memory test, participants completed a host of questionnaires: the Moral Foundations Questionnaire, the Social and Economic Conservatism Scale, the Disgust Scale-Revised, the Interpersonal Reactivity Index, and ranked their emotional responses to the vignettes (5 different emotions/vignette).
| Â | score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.00 | -5.42 – 5.42 | 1.000 |
| lag [Lag1] | 1.19 | -5.40 – 7.78 | 0.723 |
| lag [Lag2] | -0.73 | -7.32 – 5.86 | 0.829 |
| lag [Lag3] | -2.12 | -8.71 – 4.47 | 0.528 |
| category [Care-Emo] | -0.00 | -6.59 – 6.59 | 1.000 |
| category [Care-Phys] | -0.00 | -6.59 – 6.59 | 1.000 |
| category [Fairness] | -0.00 | -6.59 – 6.59 | 1.000 |
| category [Liberty] | -0.00 | -6.59 – 6.59 | 1.000 |
| category [Loyalty] | -0.00 | -6.59 – 6.59 | 1.000 |
| category [Purity] | -0.00 | -6.59 – 6.59 | 1.000 |
| category [Social Norms] | -0.00 | -6.59 – 6.59 | 1.000 |
|
lag [Lag1] * category [Care-Emo] |
-2.32 | -11.64 – 7.00 | 0.626 |
|
lag [Lag2] * category [Care-Emo] |
-6.61 | -15.93 – 2.71 | 0.164 |
|
lag [Lag3] * category [Care-Emo] |
-3.57 | -12.89 – 5.75 | 0.453 |
|
lag [Lag1] * category [Care-Phys] |
-2.95 | -12.27 – 6.37 | 0.535 |
|
lag [Lag2] * category [Care-Phys] |
-8.17 | -17.49 – 1.15 | 0.086 |
|
lag [Lag3] * category [Care-Phys] |
-11.82 | -21.14 – -2.50 | 0.013 |
|
lag [Lag1] * category [Fairness] |
0.38 | -8.94 – 9.70 | 0.936 |
|
lag [Lag2] * category [Fairness] |
1.02 | -8.30 – 10.34 | 0.830 |
|
lag [Lag3] * category [Fairness] |
2.79 | -6.53 – 12.11 | 0.557 |
|
lag [Lag1] * category [Liberty] |
-1.74 | -11.06 – 7.58 | 0.715 |
|
lag [Lag2] * category [Liberty] |
-4.48 | -13.80 – 4.84 | 0.346 |
|
lag [Lag3] * category [Liberty] |
-2.40 | -11.72 – 6.92 | 0.614 |
|
lag [Lag1] * category [Loyalty] |
-0.48 | -9.80 – 8.84 | 0.919 |
|
lag [Lag2] * category [Loyalty] |
0.27 | -9.05 – 9.59 | 0.955 |
|
lag [Lag3] * category [Loyalty] |
2.34 | -6.98 – 11.66 | 0.623 |
|
lag [Lag1] * category [Purity] |
-4.01 | -13.33 – 5.31 | 0.399 |
|
lag [Lag2] * category [Purity] |
-10.84 | -20.16 – -1.52 | 0.023 |
|
lag [Lag3] * category [Purity] |
-17.50 | -26.82 – -8.18 | <0.001 |
|
lag [Lag1] * category [Social Norms] |
-0.93 | -10.25 – 8.39 | 0.844 |
|
lag [Lag2] * category [Social Norms] |
-1.94 | -11.26 – 7.38 | 0.683 |
|
lag [Lag3] * category [Social Norms] |
-1.43 | -10.75 – 7.89 | 0.764 |
| Random Effects | |||
| σ2 | 152.62 | ||
| τ00 subID | 53.79 | ||
| N subID | 27 | ||
| Observations | 864 | ||
click here to see the full reports
| Clu. | Lag | BSR | Appro.P | Clu_Size.voxels. | Level.1 | Level.2 | Level.3 | Level.4 | Level.5 |
|---|---|---|---|---|---|---|---|---|---|
| 59 | 2 | -4.3995 | 0e+00 | 19 | Right Cerebellum | Anterior Lobe | Culmen | Gray Matter | * |
| 60 | 2 | -4.2489 | 0e+00 | 39 | Left Cerebrum | Limbic Lobe | Parahippocampal Gyrus | Gray Matter | Amygdala |
| 61 | 2 | -3.6917 | 2e-04 | 45 | Left Cerebrum | Temporal Lobe | Middle Temporal Gyrus | Gray Matter | Brodmann area 21 |
| 63 | 3 | 5.7610 | 0e+00 | 2173 | Right Cerebrum | Frontal Lobe | Precentral Gyrus | Gray Matter | Brodmann area 4 |
| 64 | 3 | 5.7452 | 0e+00 | 2181 | Right Cerebrum | Limbic Lobe | Cingulate Gyrus | Gray Matter | Brodmann area 31 |
| 91 | 3 | -7.4806 | 0e+00 | 501 | Left Cerebrum | Frontal Lobe | Inferior Frontal Gyrus | Gray Matter | Brodmann area 46 |
| 92 | 3 | -7.3120 | 0e+00 | 235 | Left Cerebrum | Sub-lobar | Claustrum | Gray Matter | * |
| 93 | 3 | -5.4190 | 0e+00 | 73 | Left Cerebrum | Limbic Lobe | Parahippocampal Gyrus | Gray Matter | Amygdala |
| 94 | 3 | -5.2782 | 0e+00 | 145 | Right Cerebrum | Frontal Lobe | Inferior Frontal Gyrus | Gray Matter | Brodmann area 46 |
| 95 | 3 | -5.0713 | 0e+00 | 22 | Left Brainstem | Midbrain | * | Gray Matter | Mammillary Body |
| 96 | 3 | -4.9481 | 0e+00 | 127 | Left Cerebrum | Temporal Lobe | Middle Temporal Gyrus | Gray Matter | Brodmann area 21 |
| 97 | 3 | -4.7988 | 0e+00 | 133 | Left Cerebrum | Frontal Lobe | Inferior Frontal Gyrus | Gray Matter | Brodmann area 44 |
| 98 | 3 | -4.7951 | 0e+00 | 35 | Left Cerebrum | Limbic Lobe | Parahippocampal Gyrus | Gray Matter | Amygdala |
| 99 | 3 | -4.3233 | 0e+00 | 59 | Left Brainstem | Midbrain | * | Gray Matter | Red Nucleus |
| 100 | 3 | -4.2745 | 0e+00 | 10 | Left Cerebrum | Frontal Lobe | Inferior Frontal Gyrus | Gray Matter | Brodmann area 45 |
| 101 | 3 | -4.1582 | 0e+00 | 19 | Right Cerebrum | Limbic Lobe | Parahippocampal Gyrus | Gray Matter | Brodmann area 30 |
| 102 | 3 | -4.0849 | 0e+00 | 16 | Right Cerebrum | Sub-lobar | Thalamus | Gray Matter | * |
| 103 | 3 | -4.0702 | 0e+00 | 23 | Right Cerebrum | Limbic Lobe | Parahippocampal Gyrus | Gray Matter | Amygdala |
| Â | score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -0.00 | -1.98 – 1.98 | 1.000 |
| lag [Lag1] | 0.92 | -1.70 – 3.55 | 0.489 |
| lag [Lag2] | -1.41 | -4.03 – 1.21 | 0.292 |
| lag [Lag3] | 1.29 | -1.33 – 3.91 | 0.334 |
| category [Care-Emo] | 0.00 | -2.62 – 2.62 | 1.000 |
| category [Care-Phys] | 0.00 | -2.62 – 2.62 | 1.000 |
| category [Fairness] | 0.00 | -2.62 – 2.62 | 1.000 |
| category [Liberty] | 0.00 | -2.62 – 2.62 | 1.000 |
| category [Loyalty] | 0.00 | -2.62 – 2.62 | 1.000 |
| category [Purity] | 0.00 | -2.62 – 2.62 | 1.000 |
| category [Social Norms] | 0.00 | -2.62 – 2.62 | 1.000 |
|
lag [Lag1] * category [Care-Emo] |
0.10 | -3.61 – 3.80 | 0.959 |
|
lag [Lag2] * category [Care-Emo] |
1.30 | -2.40 – 5.01 | 0.490 |
|
lag [Lag3] * category [Care-Emo] |
1.16 | -2.55 – 4.87 | 0.539 |
|
lag [Lag1] * category [Care-Phys] |
-1.32 | -5.03 – 2.38 | 0.484 |
|
lag [Lag2] * category [Care-Phys] |
-0.73 | -4.44 – 2.97 | 0.698 |
|
lag [Lag3] * category [Care-Phys] |
-1.82 | -5.52 – 1.89 | 0.337 |
|
lag [Lag1] * category [Fairness] |
0.59 | -3.11 – 4.30 | 0.754 |
|
lag [Lag2] * category [Fairness] |
2.06 | -1.65 – 5.76 | 0.277 |
|
lag [Lag3] * category [Fairness] |
6.60 | 2.89 – 10.31 | <0.001 |
|
lag [Lag1] * category [Liberty] |
1.18 | -2.52 – 4.89 | 0.531 |
|
lag [Lag2] * category [Liberty] |
2.11 | -1.59 – 5.82 | 0.264 |
|
lag [Lag3] * category [Liberty] |
3.90 | 0.19 – 7.60 | 0.039 |
|
lag [Lag1] * category [Loyalty] |
0.61 | -3.10 – 4.31 | 0.749 |
|
lag [Lag2] * category [Loyalty] |
1.81 | -1.90 – 5.52 | 0.339 |
|
lag [Lag3] * category [Loyalty] |
6.19 | 2.49 – 9.90 | 0.001 |
|
lag [Lag1] * category [Purity] |
0.67 | -3.03 – 4.38 | 0.722 |
|
lag [Lag2] * category [Purity] |
1.41 | -2.29 – 5.12 | 0.455 |
|
lag [Lag3] * category [Purity] |
4.51 | 0.80 – 8.22 | 0.017 |
|
lag [Lag1] * category [Social Norms] |
-4.18 | -7.89 – -0.47 | 0.027 |
|
lag [Lag2] * category [Social Norms] |
-6.42 | -10.13 – -2.72 | 0.001 |
|
lag [Lag3] * category [Social Norms] |
-13.57 | -17.28 – -9.86 | <0.001 |
| Random Effects | |||
| σ2 | 24.15 | ||
| τ00 subID | 3.43 | ||
| N subID | 27 | ||
| Observations | 864 | ||
| Clu. | Lag | BSR | Appro.P | Clu_Size.voxels. | Level.1 | Level.2 | Level.3 | Level.4 | Level.5 |
|---|---|---|---|---|---|---|---|---|---|
| 21 | 2 | 5.5611 | 0e+00 | 132 | Left Cerebrum | Limbic Lobe | Cingulate Gyrus | Gray Matter | Brodmann area 23 |
| 22 | 2 | 5.2217 | 0e+00 | 37 | Right Cerebrum | Limbic Lobe | Cingulate Gyrus | Gray Matter | Brodmann area 31 |
| 41 | 2 | 4.2821 | 0e+00 | 67 | Left Cerebrum | Occipital Lobe | Cuneus | Gray Matter | Brodmann area 7 |
| 42 | 2 | 4.2141 | 0e+00 | 30 | Right Cerebrum | Frontal Lobe | Postcentral Gyrus | Gray Matter | Brodmann area 4 |
| 43 | 2 | 4.1413 | 0e+00 | 19 | Right Cerebrum | Frontal Lobe | Middle Frontal Gyrus | Gray Matter | Brodmann area 6 |
| 44 | 2 | 4.1203 | 0e+00 | 22 | Right Cerebrum | Parietal Lobe | Precuneus | Gray Matter | Brodmann area 7 |
| 45 | 2 | 4.0541 | 1e-04 | 26 | Right Cerebrum | Frontal Lobe | Paracentral Lobule | Gray Matter | Brodmann area 5 |
| 46 | 2 | 4.0143 | 1e-04 | 54 | Left Cerebrum | Occipital Lobe | Lingual Gyrus | Gray Matter | Brodmann area 18 |
| 47 | 2 | 3.9933 | 1e-04 | 21 | Left Cerebrum | Sub-lobar | Lentiform Nucleus | Gray Matter | Putamen |
| 48 | 2 | 3.9632 | 1e-04 | 30 | Left Cerebrum | Temporal Lobe | Superior Temporal Gyrus | Gray Matter | * |
| 49 | 2 | 3.9361 | 1e-04 | 18 | Right Cerebrum | Frontal Lobe | Medial Frontal Gyrus | Gray Matter | Brodmann area 6 |
| 50 | 2 | 3.9286 | 1e-04 | 19 | Right Cerebrum | Frontal Lobe | Superior Frontal Gyrus | Gray Matter | Brodmann area 8 |
| 51 | 2 | 3.9003 | 1e-04 | 20 | Left Cerebrum | Parietal Lobe | Inferior Parietal Lobule | Gray Matter | Brodmann area 40 |
| 54 | 2 | 3.8036 | 1e-04 | 17 | Right Cerebrum | Sub-lobar | Caudate | Gray Matter | Caudate Body |
| 58 | 2 | 3.5673 | 4e-04 | 25 | Left Cerebrum | Parietal Lobe | Precuneus | Gray Matter | Brodmann area 31 |
| 61 | 2 | -3.6917 | 2e-04 | 45 | Left Cerebrum | Temporal Lobe | Middle Temporal Gyrus | Gray Matter | Brodmann area 21 |
First, I wanted to see how the pattern identified by LV1 compared to the original superordinate categorization with respect to explaining variance in moral judgments. There is not a good way to statistically compare the 3 different models (one with just a global intercept and random effects for subjects and vignettes), so this is a qualitative comparison using standard metrics for evaluating mixed model performance.
superordinateNull <- lmer(moral_decision ~ 1 + (1|subID) + (1|vigfile), data = data_old, control=lmerControl(optCtrl = list(maxeval=5000)))
superordinateOG <- lmer(moral_decision ~ superordinate + (1|subID) + (1|vigfile), data = data_old, control=lmerControl(optCtrl = list(maxeval=5000)))
superordinateNew <- lmer(moral_decision ~ superordinate_new + (1|subID) + (1|vigfile), data = data_old, control=lmerControl(optCtrl = list(maxeval=5000)))
kable(compare_performance(superordinateNull, superordinateOG, superordinateNew, bayesfactor = FALSE))
| Model | Type | AIC | BIC | R2_conditional | R2_marginal | ICC | RMSE |
|---|---|---|---|---|---|---|---|
| superordinateNull | lmerModLmerTest | 7506.270 | 7530.496 | 0.4751946 | 0.0000000 | 0.4751946 | 0.7239717 |
| superordinateOG | lmerModLmerTest | 7412.639 | 7455.034 | 0.4774696 | 0.2453677 | 0.3075696 | 0.7249011 |
| superordinateNew | lmerModLmerTest | 7385.091 | 7427.486 | 0.4766564 | 0.2805809 | 0.2725469 | 0.7253349 |
Interpretation: the neural superordinate categorization outperforms the original categorization on nearly every metric. Lower AIC & BIC = contains more information / number of parameters. R2_conditional = proportion of data explained by fixed & random effects, R2_marginal = proportion of data explained only by fixed effects (aka the one that matters here – New categories are better). ICC = amount of variance explained by random effects, lower = better fixed effects. RMSE = squared error from individual data points and model estimates.
Next, because LV1 is largely associated with emotional empathy areas, I wanted to see whether including information about participants’ emotions & trait-empathy levels improved our ability to model their moral judgments. I had to drop one participant for this analysis, so the N=26 (but the model n=2870). We asked participants to report (1-7) the following emotional reactions to each vignette: anger, fear, sadness, disgust, contempt, amusement. For empathy, I am using 2 subscores from the Interpersonal Reactivity Index: personal distress (‘self-oriented’ feelings of anxiety or unease in tense interpersonal situations) and empathic concern (‘other-oriented’ feelings of concern). I built several models encompassing a large portion of the hypothesis (as shown below). Issues with rank deficiency prohibited me from building the highest-complexity models.
modelNull <- lmer(moral_decision ~ 1 + (1|subID) + (1|vigfile), data = data_old, control=lmerControl(optCtrl = list(maxeval=5000)))
modelMoral <- lmer(moral_decision ~ category + (1|subID) + (1|vigfile), data = em_compare)
empathyModel <- lmer(moral_decision ~ PersonalDistressScore * EmpathicConcernScore +
(1|subID) + (1|vigfile), data = em_compare)
emotionModel <- lmer(moral_decision ~ angry + sad + disgusted + afraid + contemptuous + amused +
(1|subID) + (1|vigfile), data = em_compare)
empathy_category <- lmer(moral_decision ~ category * PersonalDistressScore * EmpathicConcernScore +
(1|subID) + (1|vigfile),
data = em_compare)
emotion_category <- lmer(moral_decision ~ category * angry + category * sad + category * disgusted + category * afraid + category * contemptuous + category * amused +
(1|subID) + (1|vigfile),data = em_compare)
em_explosion <- lmer(moral_decision ~ angry * sad * disgusted * afraid * contemptuous * amused *
PersonalDistressScore * EmpathicConcernScore +
(1|subID) + (1|vigfile), data=em_compare)
em_category <- lmer(moral_decision ~ category * angry + category * amused + category * sad + category * afraid + category * contemptuous + category * disgusted + category * amused +
category * PersonalDistressScore + category * EmpathicConcernScore +
angry * amused + angry * sad + angry * afraid + angry * contemptuous + angry * disgusted +
amused * sad + amused * afraid + amused * contemptuous + amused * disgusted +
sad * afraid + sad * contemptuous + sad * disgusted +
afraid * contemptuous + afraid * disgusted +
disgusted * contemptuous + (1|subID) + (1|vigfile), data = em_compare)
kable(compare_performance(modelNull, empathyModel, emotionModel, empathy_category, emotion_category, em_explosion, em_category, modelMoral, bayesfactor = FALSE, rank = TRUE))
| Model | Type | AIC | BIC | R2_conditional | R2_marginal | ICC | RMSE | Performance_Score |
|---|---|---|---|---|---|---|---|---|
| em_category | lmerModLmerTest | 6680.485 | 7217.071 | 0.5528398 | 0.4433317 | 0.1967206 | 0.6642465 | 0.7437666 |
| emotion_category | lmerModLmerTest | 6554.964 | 6906.726 | 0.5366948 | 0.4322707 | 0.1839329 | 0.6755215 | 0.7199487 |
| empathy_category | lmerModLmerTest | 6775.887 | 6984.559 | 0.5045363 | 0.3371341 | 0.2525431 | 0.7117298 | 0.5696297 |
| emotionModel | lmerModLmerTest | 6582.020 | 6641.641 | 0.4497742 | 0.1447356 | 0.3566600 | 0.6917725 | 0.5516829 |
| empathyModel | lmerModLmerTest | 6898.636 | 6940.370 | 0.4808784 | 0.0089937 | 0.4761673 | 0.7260540 | 0.4953044 |
| modelMoral | lmerModLmerTest | 6751.614 | 6817.197 | 0.4783960 | 0.3107855 | 0.2431906 | 0.7282512 | 0.4941920 |
| em_explosion | lmerModLmerTest | 7844.884 | 9389.059 | 0.5110706 | 0.2468552 | 0.3508162 | 0.6400411 | 0.4537684 |
| modelNull | lmerModLmerTest | 7506.270 | 7530.496 | 0.4751946 | 0.0000000 | 0.4751946 | 0.7239717 | 0.3718023 |
Performance score is the average of normalized values of each of these metrics, and is intended for quick, heuristic use, so we shouldn’t consider it the ultimate arbiter of model performance. However, all things considered, it does seem that ‘emotion_category’ (the model which had as fixed effects interactions between all of the emotions and category) is the best model for predicting moral judgments. It’s basically tied with em_category on R2, but has considerably better AIC & BIC. We take this to suggest that, although moral foundations do a fine job explaining variance in moral judgments, including emotional reactions to moral transgressions paints a fuller picture (even when the stimuli are specifically designed to uniquely tap into each foundation!). See below a summary of the model & trends of each of the emotions.
Note: for this model, the coefficients are sum-coded/zero-centered. So estimates reflect deviations from the grand mean of 0. Estimates for simple main effects describe their effect on all levels of the DV, rather than their relationship to a particular intercept. While this aids interpretation, it does come at a cost. We do not see estimates for the final category in the model printout. We would have to add up the estimates of every level of category & reverse the sign to get that estimate. But we do see its relationship to emotion in the model trend printouts.
| Â | moral_decision | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| Intercept | 2.41 | 2.30 – 2.53 | <0.001 |
| Social Norms | -0.99 | -1.22 – -0.77 | <0.001 |
| Authority | -0.08 | -0.22 – 0.06 | 0.256 |
| Loyalty | -0.09 | -0.22 – 0.05 | 0.217 |
| Purity | -0.01 | -0.18 – 0.16 | 0.941 |
| Care-Emo | 0.19 | 0.06 – 0.33 | 0.006 |
| Care-Phys | 0.51 | 0.38 – 0.64 | <0.001 |
| Fairness | 0.35 | 0.21 – 0.49 | <0.001 |
| anger | 0.12 | 0.06 – 0.17 | <0.001 |
| sadness | 0.05 | 0.01 – 0.09 | 0.022 |
| disgust | 0.16 | 0.11 – 0.21 | <0.001 |
| fear | -0.00 | -0.05 – 0.04 | 0.839 |
| contempt | 0.08 | 0.03 – 0.13 | 0.001 |
| amusement | -0.11 | -0.15 – -0.08 | <0.001 |
| Social Norms*anger | -0.12 | -0.38 – 0.14 | 0.369 |
| Authority*anger | 0.12 | -0.00 – 0.24 | 0.054 |
| Loyalty*anger | -0.04 | -0.17 – 0.09 | 0.521 |
| Purity*anger | -0.06 | -0.17 – 0.04 | 0.226 |
| Care-Emo*anger | 0.05 | -0.07 – 0.16 | 0.406 |
| Care-Phys*anger | -0.09 | -0.19 – 0.01 | 0.091 |
| Fairness*anger | 0.02 | -0.08 – 0.12 | 0.652 |
| Social Norms*sadness | 0.02 | -0.11 – 0.15 | 0.799 |
| Authority*sadness | 0.03 | -0.09 – 0.15 | 0.640 |
| Loyalty*sadness | -0.14 | -0.24 – -0.04 | 0.005 |
| Purity*sadness | 0.00 | -0.08 – 0.08 | 0.975 |
| Care-Emo*sadness | -0.06 | -0.14 – 0.02 | 0.169 |
| Care-Phys*sadness | 0.03 | -0.05 – 0.12 | 0.441 |
| Fairness*sadness | 0.13 | 0.02 – 0.24 | 0.026 |
| Social Norms*disgust | -0.12 | -0.28 – 0.04 | 0.144 |
| Authority*disgust | -0.07 | -0.20 – 0.06 | 0.278 |
| Loyalty*disgust | 0.09 | -0.04 – 0.22 | 0.157 |
| Purity*disgust | 0.41 | 0.30 – 0.51 | <0.001 |
| Care-Emo*disgust | 0.04 | -0.07 – 0.14 | 0.475 |
| Care-Phys*disgust | -0.09 | -0.19 – 0.00 | 0.062 |
| Fairness*disgust | -0.13 | -0.22 – -0.03 | 0.009 |
| Social Norms*fear | -0.03 | -0.18 – 0.12 | 0.702 |
| Authority*fear | -0.16 | -0.28 – -0.03 | 0.013 |
| Loyalty*fear | 0.07 | -0.05 – 0.19 | 0.245 |
| Purity*fear | 0.03 | -0.05 – 0.10 | 0.445 |
| Care-Emo*fear | -0.05 | -0.16 – 0.07 | 0.417 |
| Care-Phys*fear | -0.02 | -0.09 – 0.06 | 0.672 |
| Fairness*fear | 0.07 | -0.05 – 0.20 | 0.258 |
| Social Norms*contempt | 0.07 | -0.15 – 0.29 | 0.533 |
| Authority*contempt | -0.03 | -0.13 – 0.08 | 0.613 |
| Loyalty*contempt | 0.07 | -0.03 – 0.17 | 0.163 |
| Purity*contempt | -0.03 | -0.11 – 0.06 | 0.514 |
| Care-Emo*contempt | 0.00 | -0.09 – 0.09 | 0.957 |
| Care-Phys*contempt | -0.12 | -0.20 – -0.03 | 0.009 |
| Fairness*contempt | -0.03 | -0.11 – 0.06 | 0.540 |
| Social Norms*amusement | 0.02 | -0.05 – 0.09 | 0.551 |
| Authority*amusement | 0.06 | -0.03 – 0.15 | 0.197 |
| Loyalty*amusement | 0.06 | -0.01 – 0.14 | 0.111 |
| Purity*amusement | -0.02 | -0.10 – 0.05 | 0.533 |
| Care-Emo*amusement | -0.04 | -0.14 – 0.05 | 0.390 |
| Care-Phys*amusement | 0.13 | 0.05 – 0.21 | 0.001 |
| Fairness*amusement | -0.06 | -0.17 – 0.05 | 0.257 |
| Random Effects | |||
| σ2 | 0.48 | ||
| τ00 vigfile | 0.05 | ||
| τ00 subID | 0.06 | ||
| N subID | 26 | ||
| N vigfile | 120 | ||
| Observations | 2870 | ||
| category | angry.trend | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Social Norms | -0.0034407 | 0.1500763 | 2785.856 | -0.2977128 | 0.2908314 |
| Authority | 0.2341609 | 0.0641111 | 2728.249 | 0.1084497 | 0.3598721 |
| Loyalty | 0.0736200 | 0.0696504 | 2781.137 | -0.0629518 | 0.2101918 |
| Purity | 0.0525689 | 0.0517380 | 2799.021 | -0.0488796 | 0.1540173 |
| Care-Emo | 0.1636908 | 0.0595915 | 2751.362 | 0.0468422 | 0.2805395 |
| Care-Phys | 0.0268789 | 0.0525024 | 2753.298 | -0.0760691 | 0.1298269 |
| Fairness | 0.1388512 | 0.0504585 | 2799.623 | 0.0399115 | 0.2377909 |
| Liberty | 0.2400427 | 0.0570749 | 2793.702 | 0.1281293 | 0.3519560 |
| category | sad.trend | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Social Norms | 0.0658346 | 0.0742735 | 2804.497 | -0.0798015 | 0.2114708 |
| Authority | 0.0771864 | 0.0671872 | 2778.486 | -0.0545554 | 0.2089283 |
| Loyalty | -0.0956481 | 0.0551674 | 2787.357 | -0.2038212 | 0.0125250 |
| Purity | 0.0500664 | 0.0428555 | 2754.796 | -0.0339658 | 0.1340986 |
| Care-Emo | -0.0081407 | 0.0429548 | 2751.688 | -0.0923676 | 0.0760862 |
| Care-Phys | 0.0836920 | 0.0479696 | 2755.940 | -0.0103679 | 0.1777519 |
| Fairness | 0.1791947 | 0.0641905 | 2795.239 | 0.0533291 | 0.3050604 |
| Liberty | 0.0378270 | 0.0470167 | 2742.305 | -0.0543647 | 0.1300188 |
| category | disgusted.trend | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Social Norms | 0.0374091 | 0.0923883 | 2417.403 | -0.1437593 | 0.2185775 |
| Authority | 0.0865303 | 0.0733944 | 2744.206 | -0.0573836 | 0.2304441 |
| Loyalty | 0.2516400 | 0.0718297 | 2752.409 | 0.1107945 | 0.3924855 |
| Purity | 0.5671336 | 0.0554139 | 2739.196 | 0.4584764 | 0.6757907 |
| Care-Emo | 0.1971892 | 0.0568280 | 2743.410 | 0.0857593 | 0.3086192 |
| Care-Phys | 0.0649418 | 0.0529629 | 2800.428 | -0.0389085 | 0.1687921 |
| Fairness | 0.0307552 | 0.0514732 | 2802.189 | -0.0701740 | 0.1316843 |
| Liberty | 0.0359953 | 0.0567187 | 2791.570 | -0.0752196 | 0.1472102 |
| category | afraid.trend | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Social Norms | -0.0342723 | 0.0853140 | 2783.378 | -0.2015575 | 0.1330129 |
| Authority | -0.1623034 | 0.0705905 | 2746.043 | -0.3007192 | -0.0238875 |
| Loyalty | 0.0644893 | 0.0657475 | 2730.108 | -0.0644306 | 0.1934091 |
| Purity | 0.0240313 | 0.0363862 | 2783.379 | -0.0473153 | 0.0953779 |
| Care-Emo | -0.0528100 | 0.0649897 | 2771.333 | -0.1802431 | 0.0746231 |
| Care-Phys | -0.0201008 | 0.0339091 | 2767.741 | -0.0865905 | 0.0463889 |
| Fairness | 0.0672904 | 0.0704374 | 2757.426 | -0.0708250 | 0.2054057 |
| Liberty | 0.0746346 | 0.0491930 | 2774.817 | -0.0218239 | 0.1710931 |
| category | contemptuous.trend | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Social Norms | 0.1510839 | 0.1285451 | 2746.057 | -0.1009710 | 0.4031388 |
| Authority | 0.0535323 | 0.0570188 | 2741.616 | -0.0582719 | 0.1653365 |
| Loyalty | 0.1529521 | 0.0552647 | 2756.960 | 0.0445876 | 0.2613165 |
| Purity | 0.0525284 | 0.0433961 | 2736.804 | -0.0325640 | 0.1376208 |
| Care-Emo | 0.0829327 | 0.0487311 | 2749.401 | -0.0126206 | 0.1784859 |
| Care-Phys | -0.0347804 | 0.0449970 | 2727.369 | -0.1230120 | 0.0534512 |
| Fairness | 0.0547743 | 0.0420584 | 2735.888 | -0.0276951 | 0.1372438 |
| Liberty | 0.1303469 | 0.0459903 | 2731.799 | 0.0401676 | 0.2205263 |
| category | amused.trend | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Social Norms | -0.0923785 | 0.0338854 | 2805.132 | -0.1588213 | -0.0259356 |
| Authority | -0.0547831 | 0.0486657 | 2764.465 | -0.1502079 | 0.0406417 |
| Loyalty | -0.0503723 | 0.0413281 | 2761.505 | -0.1314094 | 0.0306648 |
| Purity | -0.1365937 | 0.0408209 | 2790.150 | -0.2166359 | -0.0565516 |
| Care-Emo | -0.1550867 | 0.0542164 | 2805.330 | -0.2613948 | -0.0487786 |
| Care-Phys | 0.0161095 | 0.0422731 | 2797.378 | -0.0667801 | 0.0989991 |
| Fairness | -0.1760015 | 0.0623840 | 2801.969 | -0.2983247 | -0.0536782 |
| Liberty | -0.2515830 | 0.0554417 | 2807.077 | -0.3602936 | -0.1428725 |
| *** | |||||
| ### Non-rotated | task PLS |
When I ran this analysis, I got this warning:
I used the coefficients (1/3,1/3,/1/3;-1/3,-1/3,-1/3 for the first (in the proper order, ofc) and 0.75,0.75,0.75,0.75,-1,-1,-1 for the second). Here are the values for lvintercorrs:
Does this mean that LVs 1 and 2 explain some of the same variance?
And for LV3, how is it possible that the mean-centered analysis would find an LV that separates social norms from the other foundations, but that an NR does not? Could it be because I didn’t include Fairness (the only marginally significant foundation in LV2) in the contrast?
*** ### Moral judgment behavioral PLS
# generalized linear mixed effect model because outcome is binary
modelMemory <- glmer(correct ~ category * old + (1 | subID),
data=data, family=binomial(link = 'logit'), control=glmerControl(optCtrl=list(maxfun=50000)))
tab_model(modelMemory, file='modelMemory.html')
| Â | correct | ||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 4.60 | 3.31 – 6.39 | <0.001 |
| category [Authority] | 0.65 | 0.46 – 0.91 | 0.013 |
| category [Loyalty] | 0.77 | 0.55 – 1.09 | 0.136 |
| category [Purity] | 1.26 | 0.87 – 1.82 | 0.222 |
| category [Care-Emo] | 1.02 | 0.71 – 1.45 | 0.928 |
| category [Care-Phys] | 0.81 | 0.57 – 1.15 | 0.236 |
| category [Fairness] | 0.91 | 0.64 – 1.29 | 0.589 |
| category [Liberty] | 0.81 | 0.57 – 1.15 | 0.240 |
| old [0] | 2.15 | 1.29 – 3.59 | 0.003 |
|
category [Authority] * old [0] |
0.57 | 0.30 – 1.10 | 0.092 |
|
category [Loyalty] * old [0] |
0.75 | 0.38 – 1.47 | 0.402 |
|
category [Purity] * old [0] |
5.71 | 1.59 – 20.50 | 0.007 |
|
category [Care-Emo] * old [0] |
1.22 | 0.58 – 2.59 | 0.596 |
|
category [Care-Phys] * old [0] |
1.22 | 0.60 – 2.51 | 0.584 |
|
category [Fairness] * old [0] |
1.69 | 0.78 – 3.66 | 0.185 |
|
category [Liberty] * old [0] |
0.89 | 0.45 – 1.78 | 0.747 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 subID | 0.31 | ||
| ICC | 0.09 | ||
| N subID | 27 | ||
| Observations | 4937 | ||
| Marginal R2 / Conditional R2 | 0.110 / 0.187 | ||
| old_pairwise | category_custom | estimate | SE | df | z.ratio | p.value |
|---|---|---|---|---|---|---|
| 1 - 0 | LibertyVsSocial | 0.1134671 | 0.3513773 | Inf | 0.322921 | 0.7467551 |
| 1 - 0 | BindingVsSocial | -0.3003486 | 0.3429769 | Inf | -0.875711 | 0.3811872 |
| 1 - 0 | IndividualizingVsSocial | -0.3089599 | 0.3061219 | Inf | -1.009271 | 0.3128448 |
| 1 - 0 | LibertyVsBinding | 0.4138158 | 0.3243490 | Inf | 1.275835 | 0.2020139 |
| 1 - 0 | LibertyVsIndividualizing | 0.4224270 | 0.2851960 | Inf | 1.481181 | 0.1385582 |
| 1 - 0 | IndividualizingVsBinding | 0.3492292 | 0.2776560 | Inf | 1.257776 | 0.2084727 |
| old_pairwise | category_custom | estimate | SE | df | z.ratio | p.value |
|---|---|---|---|---|---|---|
| 1 - 0 | FairnessVsSocial | -0.5237155 | 0.3947391 | Inf | -1.3267384 | 0.1845952 |
| 1 - 0 | EmotionVsSocial | -0.7154039 | 0.3516105 | Inf | -2.0346490 | 0.0418862 |
| 1 - 0 | NotEmotionVsSocial | 0.3184896 | 0.2900359 | Inf | 1.0981040 | 0.2721591 |
| 1 - 0 | EmotionVsNotEmotion | -1.0338935 | 0.2680813 | Inf | -3.8566421 | 0.0001150 |
| 1 - 0 | EmotionVsFairness | -0.1916884 | 0.3788561 | Inf | -0.5059663 | 0.6128803 |
| 1 - 0 | NotEmotionVsFairness | 0.8422051 | 0.3226345 | Inf | 2.6103998 | 0.0090436 |
To more fully interrogate the utility of the new superordinate categories, we decided to do model comparisons for memory and confidence as well. I suppose we could do the same for empathy and emotion. But it’s beginning to feel like a lot.. like maybe it should be two papers instead of 1. We can talk about that.
modelSupMemory <- glmer(correct ~ superordinate * old + (1 | subID),
data=data, family=binomial(link = 'logit'), control=glmerControl(optCtrl=list(maxfun=50000)))
modelSupMemoryNew <- glmer(correct ~ superordinate_new * old + (1 | subID),
data=data, family=binomial(link = 'logit'), control=glmerControl(optCtrl=list(maxfun=50000)))
modelMemoryNull <- glmer(correct ~ old + (1 | subID),
data=data, family=binomial(link = 'logit'), control=glmerControl(optCtrl=list(maxfun=50000)))
kable(compare_performance(modelMemoryNull, modelSupMemory, modelSupMemoryNew, modelMemory, bayesfactor = FALSE, rank = TRUE))
| Model | Type | AIC | BIC | R2_conditional | R2_marginal | ICC | RMSE | LOGLOSS | SCORE_LOG | Performance_Score |
|---|---|---|---|---|---|---|---|---|---|---|
| modelMemory | glmerMod | 4378.717 | 4489.293 | 0.1873817 | 0.1100216 | 0.0869236 | 0.9292995 | 0.4317988 | -Inf | 0.7162130 |
| modelSupMemoryNew | glmerMod | 4393.348 | 4451.889 | 0.1482016 | 0.0678775 | 0.0861733 | 0.9326459 | 0.4349142 | -Inf | 0.6030948 |
| modelMemoryNull | glmerMod | 4432.353 | 4451.867 | 0.1177169 | 0.0358472 | 0.0849137 | 0.9381894 | 0.4400997 | -Inf | 0.2857143 |
| modelSupMemory | glmerMod | 4431.264 | 4489.805 | 0.1261357 | 0.0447521 | 0.0851963 | 0.9367710 | 0.4387699 | -Inf | 0.2001712 |
Interpretation: using actual categories is better for predicting memory than either of the superordinate categorizations, but the neurally derived superordinate categories are indeed better at predicting successful memory than the traditional superordinate categories. See below summaries of both of the superordinate models. (traditional superordinate on the left, new superordinate on the right. still thinking about what I could call the ‘not emotion’ category…)
| Â | correct | correct | ||||
|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | CI | p | Odds Ratios | CI | p |
| (Intercept) | 4.59 | 3.31 – 6.37 | <0.001 | 4.59 | 3.31 – 6.38 | <0.001 |
| superordinate [Liberty] | 0.81 | 0.58 – 1.15 | 0.240 | |||
|
superordinate [Individualizing] |
0.91 | 0.68 – 1.21 | 0.503 | |||
| superordinate [Binding] | 0.84 | 0.63 – 1.12 | 0.236 | |||
| old [0] | 2.15 | 1.29 – 3.59 | 0.003 | 2.15 | 1.29 – 3.59 | 0.003 |
|
superordinate [Liberty] * old [0] |
0.89 | 0.45 – 1.78 | 0.746 | |||
|
superordinate [Individualizing] * old [0] |
1.35 | 0.74 – 2.45 | 0.328 | |||
|
superordinate [Binding] * old [0] |
0.82 | 0.46 – 1.46 | 0.499 | |||
|
superordinate_new [Fairness] |
0.91 | 0.64 – 1.29 | 0.590 | |||
|
superordinate_new [Emotion] |
1.00 | 0.75 – 1.34 | 0.980 | |||
|
superordinate_new [Not Emotion] |
0.74 | 0.56 – 0.98 | 0.038 | |||
|
superordinate_new [Fairness] * old [0] |
1.69 | 0.78 – 3.66 | 0.185 | |||
|
superordinate_new [Emotion] * old [0] |
1.52 | 0.82 – 2.82 | 0.180 | |||
|
superordinate_new [Not Emotion] * old [0] |
0.71 | 0.40 – 1.26 | 0.240 | |||
| Random Effects | ||||||
| σ2 | 3.29 | 3.29 | ||||
| τ00 | 0.31 subID | 0.31 subID | ||||
| ICC | 0.09 | 0.09 | ||||
| N | 27 subID | 27 subID | ||||
| Observations | 4937 | 4937 | ||||
| Marginal R2 / Conditional R2 | 0.045 / 0.126 | 0.068 / 0.148 | ||||
| Â | confresp | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.71 | 2.58 – 2.84 | <0.001 |
| category [Authority] | -0.16 | -0.31 – -0.02 | 0.027 |
| category [Loyalty] | -0.26 | -0.40 – -0.12 | <0.001 |
| category [Purity] | 0.05 | -0.09 – 0.20 | 0.451 |
| category [Care-Emo] | -0.05 | -0.19 – 0.10 | 0.522 |
| category [Care-Phys] | -0.11 | -0.26 – 0.03 | 0.118 |
| category [Fairness] | -0.05 | -0.19 – 0.10 | 0.525 |
| category [Liberty] | 0.03 | -0.11 – 0.18 | 0.648 |
| old [0] | -0.32 | -0.49 – -0.16 | <0.001 |
| correct [0] | -0.57 | -0.72 – -0.41 | <0.001 |
|
category [Authority] * old [0] |
-0.11 | -0.35 – 0.13 | 0.360 |
|
category [Loyalty] * old [0] |
0.17 | -0.07 – 0.41 | 0.170 |
|
category [Purity] * old [0] |
0.13 | -0.11 – 0.37 | 0.283 |
|
category [Care-Emo] * old [0] |
-0.13 | -0.37 – 0.11 | 0.284 |
|
category [Care-Phys] * old [0] |
-0.01 | -0.24 – 0.23 | 0.947 |
|
category [Fairness] * old [0] |
0.02 | -0.22 – 0.25 | 0.883 |
|
category [Liberty] * old [0] |
-0.05 | -0.29 – 0.18 | 0.658 |
|
category [Authority] * correct [0] |
0.01 | -0.20 – 0.22 | 0.925 |
|
category [Loyalty] * correct [0] |
0.04 | -0.17 – 0.24 | 0.734 |
|
category [Purity] * correct [0] |
0.10 | -0.13 – 0.34 | 0.396 |
|
category [Care-Emo] * correct [0] |
-0.15 | -0.37 – 0.07 | 0.171 |
|
category [Care-Phys] * correct [0] |
0.02 | -0.19 – 0.23 | 0.876 |
|
category [Fairness] * correct [0] |
0.00 | -0.21 – 0.22 | 0.994 |
|
category [Liberty] * correct [0] |
-0.07 | -0.29 – 0.14 | 0.505 |
| old [0] * correct [0] | -0.27 | -0.58 – 0.04 | 0.087 |
|
(category [Authority] old [0]) correct [0] |
0.66 | 0.26 – 1.06 | 0.001 |
|
(category [Loyalty] * old [0]) * correct [0] |
0.32 | -0.11 – 0.76 | 0.141 |
|
(category [Purity] * old [0]) * correct [0] |
-0.64 | -1.41 – 0.13 | 0.104 |
|
(category [Care-Emo] old [0]) correct [0] |
0.31 | -0.14 – 0.77 | 0.177 |
|
(category [Care-Phys] old [0]) correct [0] |
0.02 | -0.41 – 0.45 | 0.928 |
|
(category [Fairness] old [0]) correct [0] |
0.15 | -0.32 – 0.62 | 0.534 |
|
(category [Liberty] * old [0]) * correct [0] |
0.26 | -0.16 – 0.68 | 0.233 |
| Random Effects | |||
| σ2 | 0.36 | ||
| τ00 vigfile | 0.02 | ||
| τ00 subID | 0.05 | ||
| N subID | 27 | ||
| N vigfile | 183 | ||
| Observations | 4937 | ||
I think I’m going to end up using the plot with just the model estimates and no raw data, but I thought it was pretty so I’m including it here :)
modelConfidenceNull <- lmer(confresp ~ old * correct + (1 | subID) + (1|vigfile), data=data,
control=lmerControl(optCtrl = list(maxeval=5000)))
modelConfidenceSup <- lmer(confresp ~ superordinate * old * correct + (1 | subID) + (1|vigfile), data=data,
control=lmerControl(optCtrl = list(maxeval=5000)))
modelConfidenceSupNew <- lmer(confresp ~ superordinate_new * old * correct + (1 | subID) + (1|vigfile), data=data,
control=lmerControl(optCtrl = list(maxeval=5000)))
kable(compare_performance(modelConfidenceNull, modelConfidenceSup, modelConfidenceSupNew, modelConfidence, bayesfactor = FALSE, rank = TRUE))
| Model | Type | AIC | BIC | R2_conditional | R2_marginal | ICC | RMSE | Performance_Score |
|---|---|---|---|---|---|---|---|---|
| modelConfidenceSup | lmerModLmerTest | 9295.112 | 9418.698 | 0.2972411 | 0.1318083 | 0.1905488 | 0.5863438 | 0.5227183 |
| modelConfidenceSupNew | lmerModLmerTest | 9294.229 | 9417.814 | 0.2979656 | 0.1335965 | 0.1897143 | 0.5865673 | 0.5121144 |
| modelConfidenceNull | lmerModLmerTest | 9255.599 | 9301.130 | 0.2943196 | 0.1273252 | 0.1913593 | 0.5874917 | 0.5000000 |
| modelConfidence | lmerModLmerTest | 9312.866 | 9540.524 | 0.3035070 | 0.1560666 | 0.1747061 | 0.5862049 | 0.5000000 |
This time, old and new superordinate models are basically tied. I’m having trouble getting the confidence contrasts to work, so I’m going to forego them here for the sake of time.
At this point, this kind of feels like a wash. But I did it, so I figured I would include it here. Also, I’m not sure whether it makes sense to use a trait measure for behavioral PLS. Maybe we’ll have some time to talk about it.
link to cluster report